Segmented equalizer

ABSTRACT

In one embodiment of the present invention, a segmented equalizer includes a plurality of feedforward equalizer segments, each feedforward equalizer segment responsive to delayed samples of an input signal {v n }, wherein n is the index of samples, and including a filter block for filtering the delayed samples by using coefficients which are updated based on a step size generated for each equalizer segment.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to the field of adaptive equalizers andmore particularly, to adaptive equalizers for reducing multipath effectsand self-noise and increasing convergence speed when used in wirelessdata transmission environments.

2. Description of the Prior Art

Equalizers are frequently used to correct channel linear distortion. Ina wireless channel, linear distortions frequently include multipath andfading. This is due to the fact that the received signals often includesignal components that are multiply reflected in addition to theline-of-sight signal from the transmitter. Quite often, these multiplyreflected signal components do not have constant signal strengthsrelative to the line-of-the sight signal, or between themselves. Thesecause fading in the received signal.

Multipath causes fluctuations in channel frequency responses. Fadingfurther makes these fluctuations time varying.

Adaptive equalizers and adaptive decision feedback equalizers are oftenused to combat these fluctuations in channel frequency responses. Forfurther details of such equalizers, the reader is directed to followingreferences: “Adaptive Filter Theory”, Fourth Edition by Simon Haykin,Prentice Hall, 2002 and “Digital Communications”, Fourth Edition by JohnG. Proakis, McGraw-Hill, 2001.

An adaptive finite impulse response (FIR) equalizer, with length N,using Least Mean Square (LMS) update criteria works in the followingmanner.

With reference to FIG. 1, a conventional LMS equalizer 100 is shown toreceive an input signal sample sequence {v_(n)}, create multiply delayedversions of the signal samples by delay elements 110, then multiplythese multiply delayed signal samples by a set of equalizer coefficientsc₀, . . . , c_(N−1) 131, where N is equalizer length. The results arethen summed together, by a summer 115, to form an equalizer filteroutput sequence {y_(n)}. This is shown in the following Filter Equation:$\begin{matrix}{y_{n} = {\sum\limits_{i = 0}^{N - 1}{c_{n - i}c_{i}}}} & {{Eq}.\quad(1)}\end{matrix}$

The output from the equalizer, which is the equalizer filter outputsequence {y_(n)}, generated by Eq. (1), then goes through a decisiondevice Q 140 to generate the equalizer decision sequence {d_(n)}. Theequalizer decision together with equalizer filter output is used toproduce the equalizer error sequence {e_(n)} by a difference operator160 according to Error Equation:e _(n) =d _(n) −y _(n)   Eq. (2)

The equalizer error is further scaled by a step size multiplier 150 toform scaled equalizer error. The coefficients of the equalizer are thenupdated by an update means 120 using the scaled equalizer error anddelay line data (from the delay line block 110) according to the UpdateEquation:c _(i) ^(k+1) =c _(i) ^(k) +Δe _(n) v _(n−i) , i=0, . . . , N−1   Eq.(3)Where v_(n−i)'s are equalizer delay line signal samples, e_(n) isequalizer error, and Δ is step size for equalizer coefficients update.c_(i) ^(k)'s (same as c_(i)'s) and c_(i) ^(k+1)'s are current set andnext set of equalizer coefficients.

FIG. 2 shows a higher level block diagram of the equalizer 100 of FIG. 1wherein an equalizer filter and update block 290 includes a delay block240, which is the same as the delay elements 110 in FIG. 1, EqualizerFilter block 210 is the same as multiplying by the set of equalizercoefficients c₀, . . . , c_(N−1) and summation 130 of FIG. 1, thecoefficient update block 230 is the same as the update means 120 ofFIG. 1. The decision device Q 220 is the same as the decision device Q140 of FIG. 1 and the difference operator for error formation 260 anderror multiplier 250, by step size, are the same as 160 and 150,respectively in FIG. 1.

An adaptive decision feedback equalizer (DFE) using LMS update criteriaworks in a similar way. Now, the three key equations corresponding toEqs. (1)-(3) are as following for time index n:Filter Equation: $\begin{matrix}{y_{n} = {{\sum\limits_{i = 0}^{N - 1}{v_{n - i}c_{i}}} + {\sum\limits_{i = 1}^{B}{d_{n - i}b_{i}}}}} & {{Eq}.\quad(4)}\end{matrix}$Error Equation:e _(n) =d _(n) −y _(n)   Eq. (5)Update Equations:c _(i) ^(k+1) =c _(i) ^(k) +Δ _(ff) e _(n) v _(n−i) , i=0, . . . , N−1  Eq. (6)b _(i) ^(k+1) =b _(i) ^(k)+Δ_(fb) e _(n) d _(n−i) , i=1, . . . , B   Eq.(7)Where N is equalizer length for the feedforward part, B is equalizerlength for the feedback part, v_(n−i)'s are equalizer delay linesamples, d_(n−i)'s are delayed equalizer decisions, c_(i)'s areequalizer feedforward coefficients, b_(i)'s equalizer feedbackcoefficients, e_(n) is equalizer error, and Δ_(ff) and Δ_(fb) arecoefficients updates step sizes for feedforward and feedback partsrespectively. c_(i) ^(k)'s (same as c_(i)'s), b_(i) ^(k)'s (same asb_(i)'s) and c_(i) ^(k+1)'s, b_(i) ^(k+1)'s are current and next sets ofequalizer feedforward and feedback coefficients.

FIG. 3 shows an exemplary conventional decision feedback equalizer 300including a decision feedback equalizer filter and update block 330 anda feedforward filter and update block 320. The decision feedbackequalizer filter and update block 330 is shown to perform filterfunction, which is performed by the DFE Filter 302 using feedbackcoefficients from a decision feedback equalizer coefficient updatefunction 304 and the previous equalizer decision outputs {d_(n)} storedin delay line 303. The block 304 is shown to update equalizer feedbackcoefficients using equalizer error {e_(n)} scaled by a feedback stepsize Δ_(fb) using multiplier 306 as well as previous output of theequalizer decision stored in delay line 303. The feedforward filter andupdate block 320 similar to block 290 of FIG. 2 is shown to performfilter function by FFE filter 312 using sample inputs {v_(n)} stored inDelay Line 313, and feedforward coefficients from a FFE Update block314. The feedforward equalizer update block 314 is similar to the block230 of FIG. 2 for updating equalizer feedforward coefficients.

A feedforward step size multiplier 308 provides scaled equalizer errorby feedforward step size Δ_(ff) to the coefficient update function 314.Similarly, the feedforward filter block 312 is similar to the block 210of FIG. 2 and its output is provided to a summer 316 for a summationoperation with the output of the block 330 and the result of thesummation operation, at the output of the summer 316, represented by{y_(n)}, is provided to a equalizer decision block Q 310, which issimilar to the block 220 of FIG. 2. The output of the summer 316 is alsoprovided to a difference operator 318 to produce equalizer error{e_(n)}. Eq. (4) is implemented by function 312 in block 320, function302 in block 330 and summer 316, Eq. (5) is implemented by thedifference operator 318, Eq. (6) is implemented by the function 314 inblock 320, and Eq. (7) is implemented by function 304 in block 330.

When input signal sample data {v_(n)} are sampled at symbol clock rate,the equalizer is called symbol spaced equalizer. When data {v_(n)} aresampled at a clock rate faster than symbol clock, it is calledfractionally-spaced equalizer. The sample data, equalizer decisions, andcoefficients, can be real or complex.

Besides LMS coefficients updating scheme, there are other coefficientsupdating schemes such as zero-forcing (ZF), recursive least square(RLS), etc.

After initial convergence, an equalizer needs to continuously update itscoefficient to track possible changes in channel response. In multipathand fading environment, channel response can change quite fast.

In the current field of the art, equalizers are typically implementedusing devices with finite operating precision in its delay line samplesand coefficients. The equalizer typically has finite precision in itsfiltering and updating operations including multiply-and-accumulation(MAC). This problem of finite precision implementation createsadditional noise at the equalizer filter output. This noise is calledquantization noise.

During the initial convergence stage and later tracking stage, theequalizer coefficients are moving around their theoretical optimalvalues. Because of this variation and deviation from their optimalvalues, the performance of the equalizer differs from its optimal value.This non-optimal feature creates another additional noise at theequalizer filter output. This noise is called self-noise for theequalizer.

Self noise and quantization noise are the two noise factors that reducethe equalizer performance from the theoretically achievable optimalequalizer performance. They affect both initial convergence and steadystate performances.

The performance requirement of an adaptive equalizer depends on thechannel conditions, output signal to noise ratio (SNR) requirement, aswell as converging speed requirement. These requirements determineequalizer length, updating step sizes, and the precisions of itscoefficients as well as filter operations. All of these directly affectthe cost of implementing the equalizer.

The equalizer's length determines its time span. On one hand, a longerequalizer gives better theoretic steady state performance assuming thecoefficients reached their theoretical optimal state. On the other hand,a longer adaptive equalizer requires a smaller step size under the samechannel condition. A smaller step size normally results in slowerconverging speed. Therefore a longer adaptive equalizer has slowerconverging speed.

For the same step size and channel condition, a longer equalizergenerates more self-noise because more coefficients are in non-optimalstate.

A longer equalizer also requires larger precision in its coefficientsand operations. This is because each of the quantized coefficients andoperations contributes a little to the equalizer's quantization noise,and the total contribution to the quantization noise due to coefficientand operation quantization is the sum of each individual contribution.Therefore, in a conventional equalizer structure, the longer theequalizer is, the more severe the quantization noise becomes for thesame individual coefficient and operation quantization level.Equivalently, to keep the overall quantization noise level to a desiredlevel, higher coefficient and operation precision is required for alonger equalizer.

A known scheme referred to as Block Floating Point (BFP), as discussedin the publication “Implementation Options for Block Floating PointDigital Filters” by K. Ralev and P. Bauer, 1997 IEEE InternationalConference on Acoustics, Speech, and Signal Processing(ICASSP'97)—Volume 3 p. 2197, is intended to mitigate the impact offinite precision operations and quantization in data and coefficients.For a true floating point data representation, a value is represented asa mantissa part and an exponent part to represent values with largedynamic range with reasonable quantization precision. BFP uses anexponent for a block of values to achieve similar advantage. BFPessentially use a dynamic scaling for a block of data beforequantization to improve the quantization performance.

When an equalizer is implemented in BFP with block size L and number ofblocks M, the set of adaptive equalizer equations corresponding to Eqs.(1)-(3) for time index n become:Filter Equation: $\begin{matrix}{y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}^{\prime}}}} & {{Eq}.\quad(8)}\end{matrix}$

The outputs from all blocks are combined using Combination Equation, Eq.(9): $\begin{matrix}{y_{n} = {\sum\limits_{m = 0}^{M - 1}{y_{n,m}\left( a^{j_{m}} \right)}^{- 1}}} & {{Eq}.\quad(9)}\end{matrix}$Where c′_(i,m)=c_(i,m)α^(jm), α is the base for the BFP operation, j_(m)is the exponent part for block m, and α^(jm) is the equivalent scalingfor block m, and its inverse (α^(jm))⁻¹'s is the combination weightingfor block m in forming the equalizer output.

The coefficients Update Equation becomes:c′ _(i,m) ^(k+1) =c _(i,m) ^(k)+Δα^(jm) e _(n) v _(n−i) , i=0, . . . ,L−1, m=0, . . . , M−1   Eq. (10)

During the initial convergence and later on tracking, j_(m)'s arevarying, therefore corresponding monitoring and changing in thoseequations are necessary.

In general, an equalizer implemented using BFP has better trade offbetween cost and quantization noise. This approach often leads to alower overall implementation cost of an adaptive equalizer for thedesired quantization noise performance. However, because the effect ofEqs. (8)-(10) are mathematically equivalent to Eqs. (1),(3) except innumerical system representation, the dynamic behavior of an adaptiveequalizer implemented using BFP is not changed. Specifically, BFPimplementation does not affect the equalizer's self noise, convergingspeed, or tracking behavior.

In the multipath environment, only some of the equalizer coefficientshave significant values and others have zero or near zero values. Fadingfurther makes the values and the locations of those significantcoefficients time varying. Over time, some of the previously significantcoefficients may become zero or near zero and new significantcoefficients may emerge.

For conventional equalizers implementation, these features of multipathand fading environment are not utilized. The equalizers' behavior inconverging speed and self-noise are not affected by multipath andfading. This also includes the equalizers implemented using BFP eventhough such equalizers using BFP might have improved the equalizers'quantization behavior.

Sparse equalizers, discussed in U.S. Pat. No. 5,777,910, entitled“Sparse equalization filter adaptive in two dimensions” issued on Jul.7, 1998 to Cheng-Youn Lu, are introduced to combat problems associatedwith conventional equalizers. An example of a conventional sparsedecision feedback equalizer 400 is provided, in block diagram form, inFIG. 4. A sparse equalizer tries to concentrate its coefficients to aset of effective coefficient locations that have significant values, andremove all “near zero” coefficients. In FIG. 4, this is done by use ofthe switches 402 and 408, controlled by switch control block 420. Thepurpose of switches 402 and 408 is to select only those delayed datafrom FFE Delay 401 and DFE Delay 407 that correspond to the significantequalizer coefficients. Only the significant equalizer coefficients areimplemented and therefore effective. All other coefficients are set tozero and not implemented. Doing so reduces the number of requiredcoefficients because the FFE filter block 403 and the FFE update block404 as well as the DFE filter block 409 and the DFE update block 410only need to process the effective coefficients, therefore, at least intheory, there is a reduction of the converging speed, self noise, andquantization noise problems mentioned above. These features of thesparse equalizer make it particularly suitable for stationary multipathenvironment where relatively few effective coefficients are required andthe locations of those effective coefficients do not change quickly. Asparse equalizer with relatively few effective coefficients should, atleast in theory, behave like a smaller equalizer. So the problems inconverging speed, quantization noise, and self noise are all improvedcompared with a conventional equalizer with the same time span. At thesame time, the total implementation cost of a sparse equalizer may alsobe smaller compared with a conventional equalizer with the same timespan.

Two schemes are needed to implement a sparse equalizer, which isdiscussed in the Cheng-Youn Lu reference, indicated above. One scheme,not shown in FIG. 4, is to determine where to allocate the effectivecoefficients, and the other scheme is to implement switching mechanismsshown as 402 and 408 to apply those effective coefficients to generatean equalizer output.

The first scheme requires one to either sequentially learn the locationsof those effective coefficients or to periodically initialize thoseeffective locations using some training sequence. Sequentially learningthe effective locations will significantly slow down the convergence andtracking behavior of the equalizer. Periodical initialization using atraining sequence reduces the effective channel bandwidth, addsadditional costs to implementation of the equalizer, and reduces thetracking capability and overall performance if the channel changesbetween training sequences. Both approaches of selecting the effectivecoefficient locations involve additional cost compared with conventionalequalizer implementations.

The second scheme requires significantly adding complexity in theequalizer's implementation. Suppose the equalizer has 500 total possiblelocations for the coefficients yet there are 100 effective coefficients,then the equalizer needs a circuit that can dynamically switch the 100effective coefficients into any 100 of those 500 locations. Thisrequirement on dynamic switching capability together with therequirement on selecting effective coefficient locations significantlyoffsets cost savings due to a reduced number of effective coefficients.

Fading in a multipath environment further complicates the problem forsparse equalizers. For it now has to dynamically allocate itscoefficients. Fading causes the signal strength to change in bothabsolute terms and in relative terms between each signal path of themultipath signal. When the multipath environment changes, the effectivecoefficient allocations have to change accordingly and immediately.Otherwise, the sparse equalizer suffers significant performance loss.

Because a sparse equalizer has to either periodically re-allocate itscoefficients or sequentially try each of the possible locations to seewhether or not significant equalizer coefficients are needed in thoselocations, both of these methods significantly slow down the trackingability of a sparse equalizer. Therefore, a sparse equalizer performsinadequately in combating dynamic multipath and fading channel, and atthe same time adds to the cost of implementation of an equalizer.

Therefore, for the foregoing reasons, the need arises for an adaptiveequalizer having fast convergence time, low self-noise and lowerimplementation or manufacturing costs.

SUMMARY OF THE INVENTION

Briefly, a segmented equalizer including a plurality of equalizersegments is disclosed in accordance with one embodiment of the presentinvention. Each equalizer segment includes means to store delayedsamples, means for filtering the delayed samples by using coefficients,means for updating those coefficients, and means to manage an updatingstep size generated for each equalizer segment.

These and other objects and advantages of the present invention will nodoubt become apparent to those skilled in the art after having read thefollowing detailed description of the preferred embodiments illustratedin the several figures of the drawing.

IN THE DRAWINGS

FIG. 1 shows a prior art equalizer 100, in block diagram form.

FIG. 2 shows a higher level block diagram of the equalizer 100 of FIG.1.

FIG. 3 illustrates an exemplary prior art decision feedback equalizer300.

FIG. 4 shows an example of a prior art sparse decision feedbackequalizer 400, in block diagram form

FIG. 5 shows an adaptive segmented equalizer 500, in accordance with anembodiment of the present invention.

FIG. 5(a) shows further details of an equalizer segment of the equalizer500 of FIG. 5.

FIG. 5(b) shows a block floating point equalizer segment.

FIG. 6 shows a segmented decision feedback equalizer 600, in blockdiagram form and in accordance with another embodiment of the presentinvention.

FIG. 7 shows a block diagram of a decision feedback equalizer 700 inaccordance with another embodiment of the present invention.

FIG. 8 shows a segmented sparse equalizer 800 in accordance with yetanother embodiment of the present invention.

FIG. 8(a) shows further details of an equalizer segment for the sparseequalizer 800 of FIG. 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention generally describes an adaptive equalizerparticularly suitable for multipath and fading channels frequently seenin wireless data transmission environments. The equalizer comprises agroup of equalizer segments working independently of each other. Theoutputs of these equalizer segments are combined under some weighting toform a final equalizer output. This equalizer, particularly when used inmultipath and fading environments, has faster convergence, lessself-noise and quantization noise, and lower implementation cost.

An example of an adaptive equalizer of the present invention is shownand discussed relative to the various embodiments of the presentinvention, however, it should be understood that these figures anddiscussion are merely examples of the present invention and otherimplementations or forms of equalizers are anticipated without departingfrom the scope and spirit of the present invention. One such example isa FIR LMS equalizer. Applications to other types of updating schemessuch as ZF and RLS as well as DFE are anticipated and not discussed indetail, as these schemes are well known in the art.

Referring now to FIG. 5, an adaptive segmented equalizer 500 is shown,in accordance with an embodiment of the present invention, to include aplurality of feedforward equalizer segments 502, comprising of anfeedforward equalizer segment 510, a plurality of summers 509 (“adder”and “summer” are used interchangeably herein and are intended to referto the same structure and function), a difference operator 508, and adecision block Q 512. Referring to FIG. 5(a), each of the equalizersegments 510 is shown to include a multiplier 514 and an equalizersegment block 511, a step size control block 538 and a step size block515. The delayed sample output d_(o) is fed to the next equalizersegment block and the filtered output f_(o) is fed to the summer 509 ofFIG. 5.

Each one of the multipliers 514 of the equalizer segments 510 is shownto receive a step size Δ from the step size control block 538, and anerror value, denoted by {e_(n)}, which are multiplied to generate one ofthe inputs of the coefficient update block 536 in block 511. There are Mnumber of step sizes and blocks 511 shown with M being an integer value.Each of the equalizer segments 510 with one of the plurality of summersto which input is provided by a corresponding equalizer segment block511 is referred to as an equalizer stage. Thus, there are M number ofequalizer stages shown in FIG. 5. In an exemplary embodiment, theequalizer 500 of FIG. 5 is of an LMS type of equalizer although othertypes of equalizers may be employed.

The first equalizer segment 510 is shown to receive an input sequence{v_(n)} with n being a time index of samples of an input v. The input{v_(n)} is provided to the equalizer segment block 511 of the firstequalizer segment 510. As earlier indicated, a delayed sample output ofeach of the equalizer segment blocks 511 is provided as input to thenext equalizer segment block 511 and the filter output of the equalizersegment block 511, the equalizer segment filter output 513, is providedas input to a summer 509 of the same stage of equalizer segments exceptthat the equalizer segment filter output 513 of the first stage of theequalizer segment 511 is provided to the summer 509 of the next stage.The output of each of the summers 509 is received as input by the summer509 of a next equalizer stage with the output of the last stage summerbeing the equalizer filter output {y_(n)}, which is generated inaccordance with Eq. (11) below. Each of the blocks 511 includesstructures for performing equalizer segment filter and a coefficientupdate functions as well as step size control function. The output{y_(n)} is provided as input to the difference operator 508 as well asdecision block Q 512. The decision output {d_(n)} of the decision blockQ 512 is provided as another input to the difference operator 508wherein {y_(n)} is subtracted from {d_(n)} according to Eq. (13) belowto generate the error {e_(n)}, which serves as input to the variousstages of the equalizer segments for coefficient update. The output{y_(n)} serves as input to the block Q 512 where it is quantized, inaccordance with known decision schemes, generating the decision output{d_(n)} of the equalizer 500.

In FIG. 5, the adaptive equalizer 500 is segmented and the entireequalizer is first divided into some pre-determined number of segments510. Each of these equalizer segments 510 operates as if it is anindependent equalizer. All equalizer segments 510 utilize the commonequalizer error, i.e. {e_(n)}, for coefficients updates. Received data,{v_(n)} goes through these equalizer segments 510 sequentially. Theoutputs of the equalizer segments 510 are then combined to form theequalizer filter output {y_(n)}, and then placed through the decisionblock Q 512 to form the equalizer decision output {d_(n)}. One importantaspect is that each of the equalizer segments 510 has its own updatingstep size Δ_(m) for coefficient update of the corresponding segment form ranging from 0 to M−1.

Using the same notation as in Eqs. (1)-(3), the Filter Equation for eachof the equalizer segments 510 for time index n is: $\begin{matrix}{y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}}}} & {{Eq}.\quad(11)}\end{matrix}$Where m, ranging from 0 to M−1, is the segment index, M is the number ofsegments in the equalizer, and L is the segment size. For now, assumeall segments have the same segment size L. ‘i’ is a sum index used inthe summation of the multiplication results of v_(n−i,m)c_(i,m). Theoutputs of the equalizer segments 510 are then combined to form theequalizer filter output according to the Combination Equation:$\begin{matrix}{y_{n} = {\sum\limits_{m = 0}^{M - 1}y_{n,m}}} & {{Eq}.\quad(12)}\end{matrix}$The Error Equation is:e _(n) =d _(n) −y _(n)   Eq. (13)The Update Equation is:c _(i,m) ^(k+1) =c _(i,m) ^(k)+Δ_(m) e _(n) v _(n−i,m) , i=0, . . . ,L−1, m=0, . . . , M−1   Eq. (14)Where Δ_(m) is the updating step size for segment m, and it is appliedto each of the c_(i,m) belonging to segment m.

In FIG. 5, the segmented equalizer 500 comprises of a group of smallerequalizers, i.e. equalizer segments 5 10, with their outputs combined toform a single equalizer output, and using a common error forcoefficients update for all equalizer segments 510.

An important advantage of the segmented equalizer, of FIG. 5, is thatthere are different converging speeds and self noise behaviors for eachof the equalizer segments 510. In this manner, each step size Δ, foreach of the equalizer segments 510 is used to achieve desired behaviorsfor the corresponding equalizer segment 510. For instance, if the stepsizes corresponding to the effective coefficient locations as defined ina sparse equalizer are set to ‘non-zero’ values and for all others areset to zeroes, then the segmented equalizer will have the similardynamic behavior as a sparse equalizer.

In a typical multipath and fading environment, the locations of theeffective coefficients vary over time. Effective coefficients are thoseequalizer coefficients that have significant values, while non-effectivecoefficients are those that are zero or near zero. So, in our segmentedequalizer, it is not desirable to actually set the step sizescorresponding to non-effective coefficient locations to zero. Somesmaller step sizes are used for those segments to enable coefficientsc_(i,m) ^(k) to adapt to possible channel changes.

A practical arrangement is to set the step size, Δ, for each equalizersegment 510 according to the largest magnitude of coefficients in thatsegment. That is, the larger the magnitude, the larger the step size forthat segment. Accordingly, equalizer segments with larger magnitude ofcoefficients generate larger step sizes to effectuate faster convergingand tracking speed, while segments with smaller magnitudes generatesmaller step sizes to effectuate smaller self noise. Since there arerelatively few equalizer segments with large coefficient magnitudes in amultipath environment, this approach enables an equalizer, such as theequalizer 500, to have faster converging speed while generating smalleroverall self noise, and having better tracking behavior to handlechannel changes caused by fading.

One of the applications of the embodiment of FIG. 5 and otherembodiments of following figures is in the terrestrial digitaltelevision transmission area, however, other wireless data transmissionapplications are anticipated.

FIG. 5(a) shows further details, in block diagram form, of one of thefeedforward equalizer segments 510 of FIG. 5. In accordance with anembodiment of the present invention, the equalizer segment 510 is shownto include the equalizer segment block 511 and the multiplier 514. Theequalizer segment block 511 is shown to receive sample input d_(i),which may be {v_(n)} if the block 511 is in the first stage of thesegmented equalizer 500 or the output of the previous equalizer segmentstage. The multiplier 514 is shown to generate input to the block 511and to receive two inputs, one being the error input or e_(i) andanother being the step size Δ. The outputs of the equalizer segment 510are generated by the block 511 and are delayed sample output d_(o) andfilter output f_(o). The former is provided, as input, to the next stageequalizer segment 510 of the segmented equalizer 500 and the latter isthe equalizer segment filter output 513 of FIG. 5, provided to thesummers 509, as previously discussed relative to FIG. 5.

In FIG. 5(a), the block 511 is shown to include a delay line block 532,a filter block 534, a coefficient update block 536, and a step sizecontrol block 538 in accordance with an embodiment of the presentinvention. The delay line block 532 provides input to the filter block534 for filtering, and also to the block 536 for coefficient updating.The coefficients of the block 536 are used as input by the block 538 togenerate the step size Δ for use by the multiplier 514 in accordancewith the above-noted equations, as discussed relative to FIG. 5. Thestep size Δ is stored in the step size block 515 after it is provided bythe control block 538 and prior to being multiplied by the multiplier514. The output of the multiplier 514 is shown provided as another inputto the block 536 for coefficient update.

The delay line block 532 receives sample input d_(i) as its input, whichis either the input of the segmented equalizer 500, {v_(n)}, if theblock 511 is in the first stage of the equalizer or the delayed sampleoutput d_(o) of the previous stage equalizer segment block 511. Thefilter block 534 performs filtering operation in accordance with Eq.(11). Such filtering is performed using the coefficients generated bythe coefficient update block 536. The equations implemented by thefilter block 534 and the coefficients update block 536 are the same asthose discussed relative to FIG. 5. The coefficients generated by theblock 536 are also used to generate the step sizes through the step sizecontrol block 538. It is important to note that one step size isgenerated for each equalizer segment. The number of equalizercoefficients in each equalizer segment in the segmented equalizer 500can vary and can be as small as one. Different equalizer segments do notnecessarily have to have the same number of coefficients. Each equalizersegment may have different internal structure.

As previously noted, other types of equalizer segment block 511 may beemployed in the spirit of the present invention. It is convenient totreat each segment as a block in a Block Floating Point (BFP)implementation of the equalizer. In this connection, FIG. 5(b) shows theequalizer segment block 511 to include a BFP equalizer segment block 570and the multiplier 514 in accordance with another embodiment of thepresent invention. The segment block 570 is shown to receive the sameinput as that of the block 511 in FIG. 5(a), i.e. sample input d_(i) andto generate delayed sample output to the next stage equalizer segment.The block 570 is plugged in, along with a plurality of other similarblocks, to make up the plurality of equalizer segments 502 of FIG. 5.

The block 570 is shown to include a delay line block 572, a filter block574, a coefficient update block 576, a shift block 578, an exponentcontrol block 580 and a step size control block 582.

The block 572 is structurally and functionally the same as the block 532of FIG. 5(a), as is the block 574 the same as the block 534. However,the output of the filter block 574 of FIG. 5(b) is provided, as input,to the shift block 578, which also receives input from the exponentcontrol block 580 and the output of the shift block 578 becomes thefilter output 513. The exponent control block 580 monitors thecoefficients in the coefficients update block 576 and provides controlto exponent part of the coefficients. The block 580 also provides inputto the step size control block 582 with the latter receiving, as input,an output of the block 576 for coefficient magnitude information,similar to the segment 511 of FIG. 5(a). The blocks 578 and 580 causethe block floating point effect on segment filter output 513, andoperate in a manner consistent with the equations Eqs. (15)-(16)provided below.

In FIG. 5(b), BFP is applied to the segmented equalizer 500 to improvethe quantization noise performance and to reduce the implementationcost. In one embodiment of the present invention, one common exponent isutilized for each of the equalizer segments 570 and shifter 578 removesthe effect of the exponent when the segment outputs are being addedtogether by the summers 509 of FIG. 5. The shifter 578 is shown to becoupled to the blocks 574 and 580 receiving input from both andgenerating the filter output 513. It is often enough to apply BFP to thecoefficients part only so that the mantissa parts of the coefficientsfrom different segments have roughly the same magnitude with differentexponents in each segment varying. For those skilled in the art,application to both coefficients and delay line data is straightforward.The equations defining the various functions related to the segment 510and the equalizer in which it is used are set forth below as Eqs.(15)-(18) relative to FIG. 5.

For each of the equalizer segments, which includes a filter block 574,similar to the block 534, and uses a BFP feedforward equalizer segment,the function defined by a Segment Filter Equation below, is:$\begin{matrix}{y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}^{\prime}}}} & {{Eq}.\quad(15)}\end{matrix}$The output Combination Equation, i.e. generation of equalizer filteroutput {y_(n)} is: $\begin{matrix}{y_{n} = {\sum\limits_{m = 0}^{M - 1}{y_{n,m}\left( a^{j_{m}} \right)}^{- 1}}} & {{Eq}.\quad(16)}\end{matrix}$Where c′_(i,m)=c_(i,m)α^(jm), α is the base for the BFP operation, j_(m)is the exponent part for segment m. Note that α^(jm) is the equivalentscaling for segment m, and its inverse (α^(jm))⁻¹'s implemented by block578 are combination weighting for segment m in forming the equalizerfilter output {y_(n)}.

The output {y_(n)} from the segmented equalizer then goes through thedecision block Q 512 to form the equalizer decision output {d_(n)}. Theequalizer decision {d_(n)} together with equalizer filter output {y_(n)}forms the equalizer error according to Error Equation:e _(n) =d _(n) −y _(n)   Eq. (17)The coefficients Update Equation is implemented by the coefficientupdate block 576 and step size control block 582 of each segment of eachstage and is:c′ _(i,m) ^(k+1) =c′ _(i,m) ^(k)+Δ_(m)α^(jm) e _(n) v _(n−i) , i=0, . .. , L−1, m=0, . . . , M−1   Eq. (18)

Each of these equalizer segments operates as if it is a block in a blockfloating point implementation that shares a common exponential elementcontrolled by block 580. During the initial convergence and latertracking, the exponent j_(m) for each of the segments may change.Corresponding monitoring and changing are necessary, similar to BFPimplementation.

In comparing Eqs. (15), (16), (18) with Eqs. (8), (9), (10), theimportant difference is that for a segmented equalizer each segment usesa different coefficient update step size Δ_(m), while for a conventionalequalizer with BFP implementation, all blocks use the same coefficientupdate step size Δ. This difference causes dramatic changes in theequalizer's dynamic behavior.

Note that in Eqs. (16), (18) the update step size Δ_(m) and weighting(α^(jm))⁻¹ for each equalizer segment work independently, so theconverging speed as well as the contribution to the total equalizernoise due to coefficient update self-noise and due to coefficient andoperation quantization is different for each equalizer segment.Therefore, it is possible to select a proper set of parameters for eachequalizer segment so that the overall equalizer is faster in convergenceas well as has less self-noise due to coefficient update or due tocoefficient quantization.

In general, we would like a segment to have larger update step size ifthe maximum coefficient magnitude for that segment is large. Largemaximum coefficient magnitude corresponds to large j_(m). Therefore, wecan adjust Δ_(m) according to j_(m), using larger Δ_(m) for largerj_(m), and smaller Δ_(m) for smaller j_(m).

It is also possible to set Δ_(m)=Δ_(c)(α^(jm))⁻¹, Δ_(c) is apredetermined constant step size. This way, Eq. (18) becomesc′ _(i,m) ^(k+1) =c′ _(i,m) ^(k) +Δ _(c) e _(n) v _(n−i) , i=0, . . . ,L−1, m=0, . . . , M−1   Eq. (19)

The segmented equalizer 500 including the block 570 that implements Eq.(19) is simpler to implement than a conventional equalizer implementedin BFP. The segmented equalizer 500 implemented this way hasdramatically different performance compared to a conventional equalizerbecause each segment has different converging, tracking, and noisebehavior. The segmented equalizer 500 is less expensive compared to aconventional equalizer implemented using BFP, and has all benefits ofBFP in terms of quantization noise, and at the same time has fasterconverging speed, and less self noise. Therefore, it is best suited formultipath and fading environments.

In multipath and fading environments, there are only a few significantequalizer coefficients and therefore, in a segmented equalizer, thereare few segments with large step sizes and the rest of the segments havesmall step sizes. In this manner, the whole equalizer has only a feweffective coefficients. This significantly improves the initialconvergence and later on tracking performance of the equalizer, similarto a sparse equalizer. This also significantly improves the self noiseperformance since most of the coefficients have small updating stepsizes. At the same time, the segmented equalizer removes the possibilityof selecting the wrong set of effective coefficients as is possible in asparse equalizer. Therefore, the segmented equalizer works very well ina multipath and fading environment.

FIG. 6 shows a segmented decision feedback equalizer 600, in blockdiagram form and in accordance with another embodiment of the presentinvention. In one example, the equalizer 600 is of an LMS type, however,other types of adaptive equalization techniques may be employed. Most ofthe structures to the left of the decision block 612 are the same asthose of FIG. 5, and are referred to collectively as the feedforwardsection. Equalizer segments in that section are also referred to asfeedforward segments. The equalizer 600 is shown to include a pluralityof equalizer segments 602, having M equalizer segments 610, a pluralityof feedforward summers 609, a difference operator 608, and a decisionblock 612. Each of the equalizer segments 610 is shown to include amultiplier 619 and an equalizer segment block 611, one output of whichis fed to the next equalizer segment block. Each one of the multipliers619 of the equalizer segments 610 is shown to use a feedforward stepsize Δ_(ff), and an error value, denoted by {e_(n)} and multiplyingthese two to generate one of the inputs of the block 611 forcoefficients update. There are M number of step sizes and equalizersegment blocks 611 shown with M being an integer value. Each of theequalizer segments 610 with one of the plurality of summers to whichinput is provided by a corresponding equalizer segment block 611 isreferred to as an equalizer stage. Thus, there are M numbers ofequalizer stages shown in FIG. 6.

The equalizer 600 includes two distinct sections 602 and 622, and theoutputs of which are combined or added together, by the adder 609,before being quantized by the block Q 612 to generate the decisionoutput of the equalizer 600, {d_(n)}. The first section of the equalizer600, comprising the plurality of feedforward equalizer segments 602 isreferred to as a feedforward section comprising a plurality offeedforward segments 610. The second section of the equalizer 600,comprising a plurality of decision feedback equalizer segments 622 andis referred to as a feedback section having a plurality of feedbacksegments 620, which are similar structurally as the segments of thefeedforward section except that the delayed decisions instead of delayedsample inputs are stored in their delay elements. The outputs of the twosections are combined by the adder 609 to generate the input to theblock Q 612 and to the difference operator 608. The step sizes for thetwo sections are different in that there are M numbers of feedforwardstep sizes Δ_(ff) and K numbers of feedback step sizes Δ_(fb). The stepsizes are generated by each segment similar to that discussed and shownrelative to FIG. 5(a) and FIG. 5(b), and are multiplied by themultipliers 619 and 629 similar to the multipliers 514 of FIG. 5. Thatis, the step size of a particular equalizer stage is multiplied by acorresponding multiplier 619 of the same stage. The step sizes in eachof the feedforward and feedback segments are controlled individually, asstated relative to FIGS. 5, 5(a) and 5(b). Each of the segment blocks621 is coupled to receive the output of a previous segment block 621except that the first segment block of the blocks 621 in the pluralityof segments 622 is coupled to receive the output of the decision block Q612. A plurality of decision feedback summers 634 is coupled to theplurality of segments 621 in a manner similar to the manner in which thesummers 609 are coupled to the segments 611.

In FIG. 6, the number of equalizer segments in each of the feedforwardand feedback sections may vary and need not be the same. The number ofsegments bears weight on the speed of convergence of the equalizer, aspreviously discussed relative to other figures.

The first equalizer segment 610 is shown to receive an input {v_(n)}with n being a time index for samples of an input signal v. The input{v_(n)} is provided to the equalizer segment block 611 of the firstequalizer segment 610. As earlier indicated, one of the outputs of eachof the equalizer segment blocks 611 is provided as input to the nextequalizer segment block 611 and yet another output of the equalizersegment block 611, the equalizer segment filter output 613 is providedas an input to a summer 609 of the same stage of equalizer segmentsexcept that the equalizer segment filter output 613 of the first stageof the equalizer segment 611 is provided to the summer 609 of the nextstage. The output of each of the summers 609 is received as input by thesummer 609 of a next equalizer stage with the output of the last stagesummer being the filter output of the feedforward section. Each of theblocks 611 includes structures for performing equalizer segment filterand coefficient update functions similar to that discussed relative tothe blocks 511. In fact, each of the blocks 611 is the same as the block511. The output of the adder 609 of the last stage of the feedforwardsection is provided as one of the two inputs of an adder 609, whichreceives another input from the output of the last stage of the feedbacksections and adds the two to generate an equalizer filter output{y_(n)}. {y_(n)} serves as an input to the decision block Q 612 and tothe difference operator 608 where it is subtracted from the decisionoutput of the equalizer 600 {d_(n)} to generate the equalizer error{e_(n)}. The equalizer 600, when implemented in BFP structure, operatesin accordance with Eqs. (20)-(24) below.Filter Equation: $\begin{matrix}{y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}^{\prime}}}} & {{Eq}.\quad(20)}\end{matrix}$ $\begin{matrix}{x_{n,l} = {\sum\limits_{i = 1}^{L}{d_{{n - i},l}b_{i,l}^{\prime}}}} & {{Eq}.\quad(21)}\end{matrix}$The Combination Equation: $\begin{matrix}{y_{n} = {{\sum\limits_{m = 0}^{M - 1}{y_{n,m}\left( a^{j_{m}} \right)}^{- 1}} + {\sum\limits_{l = 0}^{K - 1}{x_{n,l}\left( a^{j_{l}} \right)}^{- 1}}}} & {{Eq}.\quad(22)}\end{matrix}$Error Equation:e _(n) =d _(n) −y _(n)   Eq. (23)Update Equations:c′ _(i,m) ^(k+1) =c′ _(i,m) ^(k)+Δ_(ff) _(m)α^(jm) e _(n) v _(n−i,m) ,i=0, . . . , L−1, m=0, . . . , M−1   Eq. (24)b′ _(i,l) ^(k+1) =b′ _(i,l) ^(k)+Δ_(fbl)α^(jl) e _(n) d _(n−i,l) , i=1,. . . , L,l=0, . . . , K−1   Eq. (25)Where M and K are the number of feedforward and feedback segments,respectively.

Similar discussions that led to Eq. (19) apply to DFE implementation aswell. The resulting variations of Update Equations Eqs. (24),(25) forsegmented BFP decision feedback equalizer are:c′ _(i,m) ^(k+1) =c′ _(i,m) ^(k)+Δ_(ffc) e _(n) v _(n−i,m) , i=0, . . ., L−1, m=0, . . . , M−1   Eq. (26)b′_(i,l) ^(k+1) =b′ _(i,l) ^(k)+Δ_(fhc) e _(n) d _(n−i,l) , i=1, . . . ,L,l=0, . . . , K−1   Eq. (27)Where Δ_(ffc) and Δ_(fbc) are predetermined constant feedforward andfeedback constant step sizes respectively.

Eqs. (20)-(23),(26),(27) are implemented by the segmented LMS decisionfeedback equalizer 700, in FIG. 7, in accordance with another embodimentof the present invention. FIG. 7 is similar to FIG. 6 except that themultipliers 614 are absent in FIG. 7 because the predetermined stepsizes Δ_(ffc) and Δ_(fbc) remain constants during the coefficientsupdate process and are implemented as fixed shifts inside thefeedforward segment 711 and feedback segment 721 respectively. The inputto the equalizer 700 is {v_(n)} and its output is {d_(n)} with {e_(n)}being generated by the difference operator 708, similar to equalizers ofother embodiments of the present invention discussed above.

FIG. 8 shows a segmented sparse equalizer 800 in accordance with yetanother embodiment of the present invention. In FIG. 8, the equalizer800 is shown to include a feedforward delay line block 802, a switch804, a switch control 806, a plurality of sparse feedforward equalizersegments 810, a plurality of feedforward summers 814, a decision block Q816, a decision feedback equalization delay line block 818, a decisionfeedback switch 820, a plurality of sparse decision feedback equalizersegments 824, a plurality of decision feedback summers 826, a summer 830and a difference operator 832.

In FIG. 8, the delay line block 802 is shown to receive an input to theequalizer 800, the input {v_(n)} and generates multiple outputs, whichare delayed versions of the sampled input {v_(n)}, to the switch 804,which is controlled by the switch control 806. The switch control 806also controls the switch 820. The switch 804 selects a group of delayedsamples corresponding to the locations of the effective coefficients andgenerates output to the plurality of feedforward segments 810, whichincludes segments 811, each segment being further discussed and shownrelative to FIG. 8(a). The output of the plurality of segments 810 isprovided to the summers 814. Specifically, the output of each of thesegments 811 of the plurality of segments 810 is summed with the outputof a previous stage summer with each stage being defined by segment 811and an associated summer 814. The first segment 811 of the plurality ofsegments 810 generates an output that cannot be summed with a previousstage and is thus directly fed into the summer 814 of the next stage.The output of the last summer of the plurality of summers 814 isprovided as one of the inputs of the summer 830 for summation thereofwith the output of the last summer of the plurality of the summers 826and the output of the summation is provided as input to the decisionblock Q 816, which produces the decision output of the equalizer 800, asthe output {d_(n)}. The output {d_(n)} is also shown provided to adifference operator 832. The output {d_(n)} is shown subtracted by theoutput of the summer 830 to form equalizer error {e_(n)} and the resultthereof is provided to the plurality of segments 810 and the pluralityof the segments 822. There are M feedforward segments 811 and N feedbacksegments 824 shown in FIG. 8.

In FIG. 8, the output {d_(n)} is also shown provided as input to thedelay line block 818, which produces input to the switch 820. That is,{d_(n)} is delayed, at a symbol decision time per delay and the delayedversions of the {d_(n)} is provided to the switch 820. The switch 820generates an output to the plurality of feedback segments 822, which, inturn, provide input to corresponding summers 826. The output of thesummer 832 is {e_(n)}. In FIG. 8, the switches 804 and 820 eachselectively pass through to their respective segments, those delayedsamples or decisions, from the blocks 802 and 818, respectively, thatcorrespond to the locations of the effective coefficients.

FIG. 8(a) shows further details, in block diagram form, of one of thesegments 811 of the equalizer 800 of FIG. 8, which is the same as FIG.5(a) without the delay line block 532. The segments 824 are similar tothe segments 811 except that the Delayed Sample (provided as input tothe filter 841) is replaced by Delayed Decision (from switch).

Although the segmented equalizer for FIR LMS equalizer is discussed indetail, applications to ZF and RLS equalizers, fractional spacedequalizer, as well as equalizer with complex data, or complexcoefficients, or both, are anticipated. In addition, all equalizersegments do not need to have the same step size.

Each of the equalizer segments can have a different number ofcoefficients with each segment having a segment size L, which can be thesame or a different value for each segment. The number of segments in afeedforward section or feedback section can be as small as two. An$y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}}}$equalizer segment may have only one filter coefficient with L=1.Additionally, the equalizer segments may be a combination of differenttypes, e.g., those depicted in FIG. 5(a) or FIG. 5(b), or othervariations.

Thus, in accordance with the various embodiments of the presentinvention, including but not limited to the embodiments of FIGS. 5-8(a),an adaptive equalizer structure having equalizer segments is disclosedfor particular suitability for multipath and fading channels, frequentlyseen in wireless data transmission environments. This segmentedequalizer includes a group of equalizer segments working independentlyof each other. The filter outputs of these equalizer segments are thencombined under a weighting criteria, as defined by the above-notedequations, to form a final equalizer filter output. This equalizerstructure, when used in multipath and fading environment, has fasterconvergence, less self-noise, and lower implementation cost, all at thesame time.

Although the present invention has been described in terms of specificembodiments, it is anticipated that alterations and modificationsthereof will no doubt become apparent to those skilled in the art. It istherefore intended that the following claims be interpreted as coveringall such alterations and modification as fall within the true spirit andscope of the invention.

1. A segmented equalizer comprising: a plurality of feedforwardequalizer segments, each feedforward equalizer segment responsive todelayed samples of an input signal {v_(n)}, wherein n is an index ofsamples, and including a filter block for filtering the delayed samplesby using coefficients which are updated based on a step size generatedfor each equalizer segment.
 2. A segmented equalizer, as recited inclaim 1, wherein each of the filter blocks of each of the feedforwardequalizer segments generates an output y_(n,m) that is represented by$y_{n,m} = {\sum\limits_{i = 0}^{L - 1}{v_{{n - i},m}c_{i,m}}}$ whereinn is the index of samples, m is the index of feedforward equalizersegments included in the plurality of feedforward equalizer segments,c_(i,m)'s are the coefficients for segment m, v_(n−i,m) are the delayedsample inputs in segment m and i is the index for coefficients and delayline samples in the segment.
 3. A segmented equalizer, as recited inclaim 2, wherein the plurality of feedforward equalizer segmentsincludes a first feedforward equalizer segment and a second feedforwardequalizer segment and wherein an output of each of the plurality offeedforward equalizer segments is provided as input to the nextfeedforward equalizer segment except for the first feedforward equalizersegment which receives, as input, the input signal {v_(n)}.
 4. Asegmented equalizer, as recited in claim. 3, further including aplurality of feedforward summers, each of which receives an output of acorresponding one of the plurality of feedforward equalizer segments andan output of a previous summer and adds the two outputs to generate asummer output for use by a next summer.
 5. A segmented equalizer, asrecited in claim 4, wherein the output of the summer associated with thelast summer is {y_(n)} generated by$y_{n} = {\sum\limits_{m = 0}^{M - 1}y_{n,m}}$ wherein n is the index ofsamples and m is the index of feedforward equalizer segments included inthe plurality of feedforward equalizer segments.
 6. A segmentedequalizer, as recited in claim 5, wherein each of the plurality offeedforward equalizer segments includes a coefficient update block and amultiplier, the update block being responsive to the multiplier, whichreceives an error input, and the delay block located within the samefeedforward equalizer segment for generating the coefficients for thesame equalizer segment and generating an output used for generating anupdating step size Δ_(m), the coefficients generated by:c′ _(i,m) ^(k+1) =c′ _(i,m) ^(k)+Δ_(m) e _(n) v _(n−t,m) , i=0, . . . ,L−1, m=0, . . . , M−1 Where Δ_(m) is the updating step size for segmentm and e_(n) is the error input.
 7. A segmented equalizer, as recited inclaim 6, further including a step size control block coupled to receivethe output of the coefficient update block for generating the updatingstep size Δ_(m).
 8. A segmented equalizer, as recited in claim 7,further including a decision block and a difference operator, thedecision block coupled to receive the output of the summer associatedwith the last feedforward equalizer segment and generating an equalizeroutput {d_(n)}, the difference operator for subtracting the output ofthe summer associated with the last equalizer segment from equalizeroutput {d_(n)} to generate the error {e_(n)}.
 9. A segmented equalizer,as recited in claim 5, further including a decision block, and aplurality of decision feedback equalizer segments, each decisionfeedback equalizer segment responsive to delayed decision block outputexcept a first segment which is responsive to the output of the decisionblock, and each of the decision feedback equalizer segments receivesinput from a previous one of the decision feedback equalizer segments.10. A segmented equalizer, as recited in claim 9, further including adifference operator, a second summer and a plurality of decisionfeedback summers wherein each feedback summer, except one, receives theoutput of a corresponding feedback equalizer segment and adds the sameto the output of a previous feedback summer and wherein the secondsummer receives the output of the last feedforward summer and adds it tothe output of the first feedback summer to generate the input to thedecision block and the difference operator is responsive to the outputof the second summer for subtracting the same from the output of thedecision block and generating the output of the equalizer {d_(n)}, theoutput of the difference operator serving as input to the firstfeedforward equalizer segment.
 11. A segmented equalizer, as recited inclaim 1, wherein each of the feedforward equalizer segments is a blockfloating point feedforward equalizer segment including a delay lineblock for generating the delayed sample input by receiving sample inputsfrom a previous feedforward equalizer segment except a first feedforwardequalizer segment which receives input signal {v_(n)} and providing thedelayed samples to the filter block.
 12. A segmented equalizer, asrecited in claim 11, further including a decision block and a secondsummer, the decision block coupled to receive the output of the summerassociated with the last equalizer segment and generating an equalizeroutput {d_(n)}, the second summer subtracting the output of the summerassociated with the last feedforward equalizer segment from equalizeroutput {d_(n)} to generate the error {e_(n)}.
 13. A segmented equalizer,as recited in claim 12, wherein each feedforward equalizer segmentfurther includes a shift block coupled to receive input from the filterblock and an exponent control block coupled to a coefficient updateblock and a step size control block for generating a step size, adifferent step size being generated for each segment.
 14. A segmentedequalizer, as recited in claim 13, wherein each of the filter blocks ofeach of the feedforward equalizer segments generates an output y_(n,m)defined by:$y_{n,m} = {\sum\limits_{l = 0}^{L - 1}{v_{{n - i},m}c_{i,m}^{\prime}}}$Wherein c′_(i,m) are coefficients for segment m and v_(n−i,m)'s are thedelayed input signals for segment m.
 15. A segmented equalizer, asrecited in claim 14, wherein The output Combination Equation, i.e.generation of y_(n), is:$y_{n} = {\sum\limits_{m = 0}^{M - 1}{y_{n,m}\left( a^{j_{m}} \right)}^{- 1}}$Where c′_(i,m)=c_(l,m)α^(jm), α is the base for the BFP operation, j_(m)is the exponent part for segment m, α^(jm) is the equivalent scaling forsegment m, and its inverse (α^(jm))⁻¹'s are combination weightinginforming the equalizer output.
 16. A segmented equalizer, as recited inclaim 15, wherein the coefficient update block of each segment of eachstage performs a function implementing:c′ _(l) ^(k+1) =c′ _(i) ^(k)+Δ_(m)α^(jm) e _(n) v _(i).
 17. A segmentedequalizer, as recited in claim 1, wherein the segmented equalizer is asegmented sparse equalizer.
 18. A segmented equalizer, as recited inclaim 17, including a first switch coupled to the plurality of segmentsfor selectively providing non-zero delayed samples.
 19. A segmentedequalizer comprising: a plurality of feedback equalizer segments, eachfeedback equalizer segment responsive to delayed equalizer decision{d_(n)}, wherein n is an index of decisions, and including a filterblock for filtering the delayed decisions by using coefficients based ona step size generated for each feedback equalizer segment.
 20. Anequalization method comprising: receiving delayed samples by equalizersegments of a segmented equalizer; in each of the equalizer segments,filtering the delayed samples; in each of the equalizer segments,updating coefficients for use in the filtering step; and generating astep sizes in each of the equalizer segments for use in the updatingstep.